This library provides:

• a unified Python interface for (standard) CPU and GPU BLAS dense linear algebra libraries
• a unified Python interface for (custom) CPU and GPU proximal operator libraries
• Python implementations of projection and matrix equilibration routines
• a Python implementation of the ADMM solver POGS, based on Chris Fougner's convex optimization solver library (http:/github.com/foges/pogs).

optkit's C/CUDA backend libraries have the following dependencies:

cBLAS
CUDA >= 7.5

optkit's Python package has the following additional dependencies:

python >= 2.7
numpy >= 1.8
scipy >= 0.13
toolz	

### Building from source

To download optkit from GitHub:

git clone https://github.com/bungun/python-pogs --recursive

Alternately, you can execute

git clone https://github.com/bungun/optkit

If would like to compile POGS for GPU, please make sure your $PATH environment variable contains the path to CUDA binaries (namely nvcc) and libraries.

$ export PATH=$PATH:<path-to-cuda-bin>
$ export LD\_LIBRARY\_PATH=$LD\_LIBRARY\_PATH:<path-to-cuda-libs> 

(For instance, on Linux distributions, these paths would be /usr/local/cuda/bin and /usr/local/cuda/lib64, respectively.)

In the shell, navigate to the directory:

$ cd <path-to-optkit>/python

If desired, create a virtual environment before the installation step (https://virtualenv.pypa.io/en/latest/).

$ [sudo] PATH=$PATH python setup.py install

After installing, import the package pogs to use it in a script, e.g.:

> import optkit as ok

The import should print a success messages:

> optkit backend set to cpu64

(TODO: further explanation)

To change the default backend for the optkit module to attempt to bind upon import, set the following environment variables:

$ export OPTKIT_DEFAULT_DEVICE=<device>

(values = cpu or gpu; optkit defaults to CPU if environment variable not set)

$ export OPTKIT_DEFAULT_FLOATBITS=<bits>

(values = 64 or 32; optkit defaults to 64-bit floating point precision if environment variable not set).

By default, optkit's linear algebra and proximal operator Python methods check argument types, dimensions and device (i.e., CPU- vs GPU-allocated pointers) before calling the optkit C/CUDA libraries. To suppress one or more of these behaviors (e.g., for debugged Python scripts), set the following environment variables:

$ export OPTKIT_CHECK_TYPE=0
$ export OPTKIT_CHECK_DIM=0
$ export OPTKIT_CHECK_DEVICE=0

optkit's dense linear algebra libraries wrap GNU CBlas and cudaBLAS.

Most of the linear algebra libraries, proximal operator libraries are C/CUDA adaptations of libraries implemented in C++/CUDA in POGS.

The projection, matrix equilibration, and POGS solver algorithms are Python adaptations of the corresponding C++ versions implemented in POGS.

Visit http://foges.github.io/pogs/ for detailed information on POGS

The following people have been, and are, involved in the development and maintenance of optkit

• Baris Ungun (principal developer)
• Stephen Boyd (methods and math)